chatbot.py

import json
import numpy as np
import nltk
from nltk.stem import WordNetLemmatizer
from tensorflow.keras.models import load_model
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Activation, Dropout
from tensorflow.keras.optimizers import SGD
import random
import pickle

lemmatizer = WordNetLemmatizer()

# Load intents file
intents = json.loads(open('intents.json').read())

# Try to load the existing model, if it doesn't exist, create it
try:
    model = load_model('chatbot_model.h5')
    words = pickle.load(open('words.pkl', 'rb'))
    classes = pickle.load(open('classes.pkl', 'rb'))
except:
    print("No existing model found, please run train_chatbot.py to create the model first.")
    model = None

def clean_up_sentence(sentence):
    sentence_words = nltk.word_tokenize(sentence)
    sentence_words = [lemmatizer.lemmatize(word.lower()) for word in sentence_words]
    return sentence_words

def bag_of_words(sentence, words):
    sentence_words = clean_up_sentence(sentence)
    bag = [0] * len(words)
    for s in sentence_words:
        for i, w in enumerate(words):
            if w == s:
                bag[i] = 1
    return np.array(bag)

def predict_class(sentence, model):
    if model is None:
        return [{"intent": "no_model", "probability": "0"}]

    p = bag_of_words(sentence, words)
    res = model.predict(np.array([p]))[0]
    ERROR_THRESHOLD = 0.25
    results = [[i, r] for i, r in enumerate(res) if r > ERROR_THRESHOLD]
    results.sort(key=lambda x: x[1], reverse=True)
    return_list = []
    for r in results:
        return_list.append({"intent": classes[r[0]], "probability": str(r[1])})
    return return_list

def get_response(ints, intents_json):
    tag = ints[0]['intent']
    list_of_intents = intents_json['intents']
    for i in list_of_intents:
        if i['tag'] == tag:
            result = random.choice(i['responses'])
            break
    return result